Estimation of the Area and Precipitation Associated with a Tropical Cyclone Biparjoy by using Image Processing

• URL: http://arxiv.org/abs/2407.05255v1
• Date: Sun, 7 Jul 2024 04:41:27 GMT
• Title: Estimation of the Area and Precipitation Associated with a Tropical Cyclone Biparjoy by using Image Processing
• Authors: Shikha Verma, Kuldeep Srivastava, Akhilesh Tiwari, Shekhar Verma,
• Abstract summary: This paper proposes an approach to estimate the accumulated precipitation and impact on affected area using Remote Sensing data. Image processing techniques were employed to identify and extract precipitation clusters linked to the cyclone. Results indicate that Biparjoy contributed a daily average rainfall of 53.14 mm/day across India and the Arabian Sea, with the Indian boundary receiving 11.59 mm/day, covering an extensive 411.76 thousand square kilometers.
• Score: 6.68100259034081
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The rainfall associated with Topical Cyclone(TC) contributes a major amount to the annual rainfall in India. Due to the limited research on the quantitative precipitation associated with Tropical Cyclones (TC), the prediction of the amount of precipitation and area that it may cover remains a challenge. This paper proposes an approach to estimate the accumulated precipitation and impact on affected area using Remote Sensing data. For this study, an instance of Extremely Severe Cyclonic Storm, Biparjoy that formed over the Arabian Sea and hit India in 2023 is considered in which we have used the satellite images of IMERG-Late Run of Global Precipitation Measurement (GPM). Image processing techniques were employed to identify and extract precipitation clusters linked to the cyclone. The results indicate that Biparjoy contributed a daily average rainfall of 53.14 mm/day across India and the Arabian Sea, with the Indian boundary receiving 11.59 mm/day, covering an extensive 411.76 thousand square kilometers. The localized intensity and variability observed in states like Gujarat, Rajasthan, Madhya Pradesh, and Uttar Pradesh highlight the need for tailored response measures, emphasizing the importance of further research to enhance predictive models and disaster readiness, crucial for building resilience against the diverse impacts of tropical cyclones.

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